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Статті в журналах з теми "Structural causal models"
Beckers, Sander. "Equivalent Causal Models." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 7 (May 18, 2021): 6202–9. http://dx.doi.org/10.1609/aaai.v35i7.16771.
Повний текст джерелаVansteelandt, S., and E. Goetghebeur. "Causal inference with generalized structural mean models." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 65, no. 4 (October 28, 2003): 817–35. http://dx.doi.org/10.1046/j.1369-7412.2003.00417.x.
Повний текст джерелаHUBER, FRANZ. "STRUCTURAL EQUATIONS AND BEYOND." Review of Symbolic Logic 6, no. 4 (July 8, 2013): 709–32. http://dx.doi.org/10.1017/s175502031300018x.
Повний текст джерелаRobins, James M., Miguel Ángel Hernán, and Babette Brumback. "Marginal Structural Models and Causal Inference in Epidemiology." Epidemiology 11, no. 5 (September 2000): 550–60. http://dx.doi.org/10.1097/00001648-200009000-00011.
Повний текст джерелаRothenhäusler, Dominik, Jan Ernest, and Peter Bühlmann. "Causal inference in partially linear structural equation models." Annals of Statistics 46, no. 6A (December 2018): 2904–38. http://dx.doi.org/10.1214/17-aos1643.
Повний текст джерелаNeugebauer, Romain, and Mark van der Laan. "Nonparametric causal effects based on marginal structural models." Journal of Statistical Planning and Inference 137, no. 2 (February 2007): 419–34. http://dx.doi.org/10.1016/j.jspi.2005.12.008.
Повний текст джерелаZheng, Cheng, David C. Atkins, Xiao-Hua Zhou, and Isaac C. Rhew. "Causal Models for Mediation Analysis: An Introduction to Structural Mean Models." Multivariate Behavioral Research 50, no. 6 (November 2, 2015): 614–31. http://dx.doi.org/10.1080/00273171.2015.1070707.
Повний текст джерелаTalbot, Denis, Amanda M. Rossi, Simon L. Bacon, Juli Atherton, and Geneviève Lefebvre. "A graphical perspective of marginal structural models: An application for the estimation of the effect of physical activity on blood pressure." Statistical Methods in Medical Research 27, no. 8 (December 29, 2016): 2428–36. http://dx.doi.org/10.1177/0962280216680834.
Повний текст джерелаBazinas, Vassilios, and Bent Nielsen. "Causal Transmission in Reduced-Form Models." Econometrics 10, no. 2 (March 24, 2022): 14. http://dx.doi.org/10.3390/econometrics10020014.
Повний текст джерелаSteyer, Rolf. "Analyzing Individual and Average Causal Effects via Structural Equation Models." Methodology 1, no. 1 (January 2005): 39–54. http://dx.doi.org/10.1027/1614-1881.1.1.39.
Повний текст джерелаДисертації з теми "Structural causal models"
Oberst, Michael Karl. "Counterfactual policy introspection using structural causal models." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/124128.
Повний текст джерелаThesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 97-102).
Inspired by a growing interest in applying reinforcement learning (RL) to healthcare, we introduce a procedure for performing qualitative introspection and `debugging' of models and policies. In particular, we make use of counterfactual trajectories, which describe the implicit belief (of a model) of 'what would have happened' if a policy had been applied. These serve to decompose model-based estimates of reward into specific claims about specific trajectories, a useful tool for 'debugging' of models and policies, especially when side information is available for domain experts to review alongside the counterfactual claims. More specically, we give a general procedure (using structural causal models) to generate counterfactuals based on an existing model of the environment, including common models used in model-based RL. We apply our procedure to a pair of synthetic applications to build intuition, and conclude with an application on real healthcare data, introspecting a policy for sepsis management learned in the recently published work of Komorowski et al. (2018).
by Michael Karl Oberst.
S.M.
S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
Odondi, Lang'O. "Causal modelling of survival data with informative noncompliance." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/causal-modelling-of-survival-data-with-informative-noncompliance(74f40dc0-e5d1-46c0-ab2f-ac42a3425ac7).html.
Повний текст джерелаAten, Jason Erik. "Causal not confounded gene networks inferring acyclic and non-acyclic gene bayesian networks in mRNA expression studies using recursive v-structures, genetic variation, and orthogonal causal anchor structural equation models /." Diss., Restricted to subscribing institutions, 2007. http://proquest.umi.com/pqdweb?did=1563274791&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.
Повний текст джерелаEwings, F. M. "Practical and theoretical considerations of the application of marginal structural models to estimate causal effects of treatment in HIV infection." Thesis, University College London (University of London), 2012. http://discovery.ucl.ac.uk/1346448/.
Повний текст джерелаRosich, Oliva Albert. "Sensor placement for fault diagnosis based on structural models: application to a fuel cell stak system." Doctoral thesis, Universitat Politècnica de Catalunya, 2011. http://hdl.handle.net/10803/53635.
Повний текст джерелаEl present treball té per objectiu incrementar les prestacions dels diagnosticadors mitjançant la localització de sensors en el procés. D'aquesta manera, instal·lant els sensors apropiats s'obtenen millors diagnosticador i més facilitats d'implementació. El treball està basat en models estructurals i contempla una sèrie de simplificacions per tal de entrar-se només en la problemàtica de la localització de sensors. S'utilitzen diversos enfocs per tal de resoldre la localització de sensors, tot ells tenen com objectiu trobar la configuració òptima de sensors. Les tècniques de localització de sensors són aplicades a un sistema basat en una pila de combustible. El model d'aquest sistema està format per equacions no lineals. A més, hi ha la possibilitat d'instal·lar fins a 30 sensors per tal de millorar la diagnosis del sistema. Degut a aquestes característiques del sistema i del model, els resultats obtinguts mitjançant aquest cas d'estudi reafirmen l'aplicabilitat dels mètodes proposats.
Dubois, Florent. "Dynamic models of segregation." Thesis, Aix-Marseille, 2017. http://www.theses.fr/2017AIXM0313.
Повний текст джерелаThis thesis studies the causes and consequences of the residential segregation process in the post-Apartheid South Africa.Inside this general issue, we are interested in several aspects still debated in the literature on residential segregation. Thefirst concerns the impact of individuals’ preferences for the racial composition of their neighborhood on the segregationlevels. The second question deals with the impact of residential segregation on the income levels of each racial group. Thelast issue is related to quantifying the different causes of segregation.Three chapters constitute this thesis. In the first chapter, we reconcile the theoretical literature on the impact of preferencesfor the racial composition of the neighborhood with the empirical evidences of declining levels of segregation in theUnited-States and South Africa. We argue that if individuals internalize the economic and social life that a new entrantbrings with him, then integrated neighborhoods can emerge. This effect is empirically stronger than homophilly andracism. In the second chapter, we study the impact of residential segregation on the whole income distribution. We showthat residential segregation has a positif effect on top incomes for Whites, whereas it has a negatif effect for Blacks at thebottom of the distribution. The effect of residential segregation is even more important than the effect of education inmost cases. In the third chapter, we quantify the impact of each determinant of segregation. We find that the lackof access to basic public services is the main determinant, whereas differences in sociodemographics only account for asmall part in the most segregated areas
Oba, Koji. "How to use marginal structural models in randomized trials to estimate the natural direct and indirect effects of therapies mediated by causal intermediates." 京都大学 (Kyoto University), 2011. http://hdl.handle.net/2433/152045.
Повний текст джерелаBailly, Sébastien. "Utilisation des antifongiques chez le patient non neutropénique en réanimation." Thesis, Université Grenoble Alpes (ComUE), 2015. http://www.theses.fr/2015GREAS013/document.
Повний текст джерелаCandida species are among the main pathogens isolated from patients in intensive care units (ICUs) and are responsible for a serious systemic infection: invasive candidiasis. A late and unreliable diagnosis of invasive candidiasis aggravates the patient's status and increases the risk of short-term death. The current guidelines recommend an early treatment of patients with high risks of invasive candidiasis, even in absence of documented fungal infection. However, increased antifungal drug consumption is correlated with increased costs and the emergence of drug resistance whereas there is yet no consensus about the benefits of the probabilistic antifungal treatment.The present work used modern statistical methods on longitudinal observational data. It investigated the impact of systemic antifungal treatment (SAT) on the distribution of the four Candida species most frequently isolated from ICU patients', their susceptibilities to SATs, the diagnosis of candidemia, and the prognosis of ICU patients. The use of autoregressive integrated moving average (ARIMA) models for time series confirmed the negative impact of SAT use on the susceptibilities of the four Candida species and on their relative distribution over a ten-year period. Hierarchical models for repeated measures showed that SAT has a negative impact on the diagnosis of candidemia: it decreases the rate of positive blood cultures and increases the time to positivity of these cultures. Finally, the use of causal inference models showed that early SAT has no impact on non-neutropenic, non-transplanted patient prognosis and that SAT de-escalation within 5 days after its initiation in critically ill patients is safe and does not influence the prognosis
Bergman, Ruth. "Learning models of environments with manifest causal structure." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/36559.
Повний текст джерелаIncludes bibliographical references (leaves 188-192).
by Ruth Bergman.
Ph.D.
Baltar, Valéria Troncoso. "Equações estruturais aplicadas a modelos causais de câncer de pulmão." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/6/6132/tde-01032011-150337/.
Повний текст джерелаBackground: Lung cancer (LC) continues to be the most common cancer death in the world. Tobacco exposure continues to be the most important cause. In addition, micronutrient intake has been linked to LC, because they are the main source of vitamins and amino acids involved in the one-carbon metabolism (OCM) which is considered key in maintaining DNA integrity, regulating gene expression, and may thus affect carcinogenesis. Immune activation is involved in the aging process in normal healthy individuals as well as in a number of pathologies, including cancer. Objectives: To investigate how OCM, immune activation and tobacco are related to LC incidence in a nested case-control study from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Methods: To validate plasma cotinine levels as a good biomarker for tobacco exposure, a generalized linear model was applied. To evaluate the effects of tobacco, OCM and immune activation in LC, structural equation models (SEM) were applied in two different ways. Results: Based on questions about smoking, passive smoking and number of cigarettes smoked, it was shown that cotinine is a good biomarker for tobacco exposure (passive and active exposure with significant relation, p<0.001 and P<0.001, respectively). In a SEM model with only observed variables, including OCM and immune activation, methionine and folate as proximal causes presented a strong and inverse relation with LC risk. An increase in one standard deviation of serum levels of methionine and folate meant a 19 per cent (P<0.01) and 12 per cent (P<0.01) reduction in LC risk, respectively. In a SEM including latent variables (each one including vitamins and amino acids important to promote DNA methylation, nucleotide synthesis and immune activity), a direct and protective effect for DNA methylation (p=0.018) and immune activation was found (p=0.037), whereas nucleotide synthesis did not present a significant total effect. In both approaches of SEM, tobacco exposure remains with the highest impact on LC risk. Conclusions: It was found that cotinine is a good biomarker of tobacco exposure (active and passive). It was confirmed that methylation protects against LC. Immune activation presented a direct protective effect in the latent model, while nucleotide synthesis was not confirmed to be related to LC risk. Tobacco effect remains as the factor with highest impact in lung cancer
Книги з теми "Structural causal models"
Linear causal modeling with structural equations. Boca Raton, FL: Chapman & Hall/CRC, 2009.
Знайти повний текст джерелаJ, Thomas J. The links between structural adjustment and poverty: Causal or remedial? [Santiago, Chile]: PREALC, 1993.
Знайти повний текст джерелаLeonovich, Sergey, Evgeniy Shalyy, Elena Polonina, Elena Sadovskaya, Lev Kim, and Valentin Dorkin. Durability of port reinforced concrete structures (Far East and Sakhalin). ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1816638.
Повний текст джерелаVarlamov, Oleg. Mivar databases and rules. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.
Повний текст джерелаKravchenko, Igor', Maksim Glinskiy, Sergey Karcev, Viktor Korneev, and Diana Abdumuminova. Resource-saving plasma technology in the repair of processing equipment. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1083289.
Повний текст джерелаSang-in, Chŏn, ред. Hanʼguk hyŏndaesa: Chinsil kwa haesŏk. Kyŏnggi-do Pʻaju-si: Nanam Chʻulpʻan, 2005.
Знайти повний текст джерелаShimizu, Shohei. Semiparametric Structural Equation Models for Causal Discovery. Springer, 2021.
Знайти повний текст джерелаShimizu, Shohei. Semiparametric Structural Equation Models for Causal Discovery. Springer London, Limited, 2017.
Знайти повний текст джерелаBollen, Kenneth A., Sophia Rabe‐Hesketh, and Anders Skrondal. Structural Equation Models. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0018.
Повний текст джерелаCoseru, Christian. Consciousness and Causal Emergence. Edited by Jonardon Ganeri. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199314621.013.24.
Повний текст джерелаЧастини книг з теми "Structural causal models"
Bergsma, Wicher, Marcel Croon, and Jacques A. Hagenaars. "Causal Analyses: Structural Equation Models and (Quasi-)Experimental Designs." In Marginal Models, 155–90. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/b12532_5.
Повний текст джерелаRobins, James M. "Marginal Structural Models versus Structural nested Models as Tools for Causal inference." In Statistical Models in Epidemiology, the Environment, and Clinical Trials, 95–133. New York, NY: Springer New York, 2000. http://dx.doi.org/10.1007/978-1-4612-1284-3_2.
Повний текст джерелаShimizu, Shohei. "Non-Gaussian Structural Equation Models for Causal Discovery." In Statistics and Causality, 153–84. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781118947074.ch7.
Повний текст джерелаHelian, Shanjun, Babette A. Brumback, Matthew C. Freeman, and Richard Rheingans. "Structural Nested Models for Cluster-Randomized Trials." In Statistical Causal Inferences and Their Applications in Public Health Research, 169–86. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41259-7_9.
Повний текст джерелаStern, Hal S., and Yoonsook Jeon. "Applying Structural Equation Models with Incomplete Data." In Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives, 331–42. Chichester, UK: John Wiley & Sons, Ltd, 2005. http://dx.doi.org/10.1002/0470090456.ch30.
Повний текст джерелаNarendra, Tanmayee, Prerna Agarwal, Monika Gupta, and Sampath Dechu. "Counterfactual Reasoning for Process Optimization Using Structural Causal Models." In Lecture Notes in Business Information Processing, 91–106. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26643-1_6.
Повний текст джерелаValente, Bruno Dourado, and Guilherme Jordão de Magalhães Rosa. "Mixed Effects Structural Equation Models and Phenotypic Causal Networks." In Methods in Molecular Biology, 449–64. Totowa, NJ: Humana Press, 2013. http://dx.doi.org/10.1007/978-1-62703-447-0_21.
Повний текст джерелаWu, Pan, and Xin M. Tu. "Structural Functional Response Models for Complex Intervention Trials." In Statistical Causal Inferences and Their Applications in Public Health Research, 217–38. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41259-7_12.
Повний текст джерелаCarter, Christopher L., and Thad Dunning. "Instrumental Variables: From Structural Equation Models to Design-Based Causal Inference." In The SAGE Handbook of Research Methods in Political Science and International Relations, 748–68. 1 Oliver's Yard, 55 City Road London EC1Y 1SP: SAGE Publications Ltd, 2020. http://dx.doi.org/10.4135/9781526486387.n43.
Повний текст джерелаHair, Joseph F., G. Tomas M. Hult, Christian M. Ringle, Marko Sarstedt, Nicholas P. Danks, and Soumya Ray. "An Introduction to Structural Equation Modeling." In Classroom Companion: Business, 1–29. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80519-7_1.
Повний текст джерелаТези доповідей конференцій з теми "Structural causal models"
Yang, Mengyue, Furui Liu, Zhitang Chen, Xinwei Shen, Jianye Hao, and Jun Wang. "CausalVAE: Disentangled Representation Learning via Neural Structural Causal Models." In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2021. http://dx.doi.org/10.1109/cvpr46437.2021.00947.
Повний текст джерелаHeyn, Hans-Martin, and Eric Knauss. "Structural causal models as boundary objects in AI system development." In CAIN '22: 1st Conference on AI Engineering - Software Engineering for AI. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3522664.3528615.
Повний текст джерелаLee, Burton Hoyt. "Design FMEA for Mechatronic Systems Using Bayesian Network Causal Models." In ASME 1999 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/detc99/dac-8605.
Повний текст джерелаLaurent, Jonathan, Jean Yang, and Walter Fontana. "Counterfactual Resimulation for Causal Analysis of Rule-Based Models." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/260.
Повний текст джерелаIbeling, Duligur, and Thomas Icard. "On the Conditional Logic of Simulation Models." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/258.
Повний текст джерелаLübke, Karsten, Bianca Krol, and Sandra Sülzenbrück. "Draw (Causal) Conclusions from Data – Some Evidence." In IASE 2021 Satellite Conference: Statistics Education in the Era of Data Science. International Association for Statistical Education, 2022. http://dx.doi.org/10.52041/iase.ujhqs.
Повний текст джерелаCai, Ruichu, Jie Qiao, Kun Zhang, Zhenjie Zhang, and Zhifeng Hao. "Causal Discovery with Cascade Nonlinear Additive Noise Model." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/223.
Повний текст джерелаHuegle, Johannes, Christopher Hagedorn, and Matthias Uflacker. "How Causal Structural Knowledge Adds Decision-Support in Monitoring of Automotive Body Shop Assembly Lines." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/758.
Повний текст джерелаBochman, Alexander. "Actual Causality in a Logical Setting." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/239.
Повний текст джерелаKavicka, Frantisek, Karel Stransky, Bohumil Sekanina, Jana Dobrovska, and Josef Stetina. "Cooling of a Massive Casting of Ductile Cast-Iron and Its Numerical Optimization." In ASME 2009 Pressure Vessels and Piping Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/pvp2009-77914.
Повний текст джерелаЗвіти організацій з теми "Structural causal models"
Clarke, Paul S., and Frank Windmeijer. Identification of causal effects on binary outcomes using structural mean models. Institute for Fiscal Studies, March 2010. http://dx.doi.org/10.1920/wp.cem.2010.0210.
Повний текст джерелаNaugle, Asmeret, Laura Swiler, Kiran Lakkaraju, Stephen Verzi, Christina Warrender, and Vicente Romero. Graph-Based Similarity Metrics for Comparing Simulation Model Causal Structures. Office of Scientific and Technical Information (OSTI), August 2022. http://dx.doi.org/10.2172/1884926.
Повний текст джерелаTriplett, Josh. Relativistic Causal Ordering A Memory Model for Scalable Concurrent Data Structures. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.497.
Повний текст джерелаMontville, Thomas J., and Roni Shapira. Molecular Engineering of Pediocin A to Establish Structure/Function Relationships for Mechanistic Control of Foodborne Pathogens. United States Department of Agriculture, August 1993. http://dx.doi.org/10.32747/1993.7568088.bard.
Повний текст джерелаBao, Jieyi, Xiaoqiang Hu, Cheng Peng, Yi Jiang, Shuo Li, and Tommy Nantung. Truck Traffic and Load Spectra of Indiana Roadways for the Mechanistic-Empirical Pavement Design Guide. Purdue University, 2020. http://dx.doi.org/10.5703/1288284317227.
Повний текст джерелаNantung, Tommy E., Jusang Lee, John E. Haddock, M. Reza Pouranian, Dario Batioja Alvarez, Jongmyung Jeon, Boonam Shin, and Peter J. Becker. Structural Evaluation of Full-Depth Flexible Pavement Using APT. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317319.
Повний текст джерелаGutnick, David, and David L. Coplin. Role of Exopolysaccharides in the Survival and Pathogenesis of the Fire Blight Bacterium, Erwinia amylovora. United States Department of Agriculture, September 1994. http://dx.doi.org/10.32747/1994.7568788.bard.
Повний текст джерелаRahmani, Mehran, Xintong Ji, and Sovann Reach Kiet. Damage Detection and Damage Localization in Bridges with Low-Density Instrumentations Using the Wave-Method: Application to a Shake-Table Tested Bridge. Mineta Transportation Institute, September 2022. http://dx.doi.org/10.31979/mti.2022.2033.
Повний текст джерелаZhang, Xingyu, Matteo Ciantia, Jonathan Knappett, and Anthony Leung. Micromechanical study of potential scale effects in small-scale modelling of sinker tree roots. University of Dundee, December 2021. http://dx.doi.org/10.20933/100001235.
Повний текст джерелаPARSHUTKINA, T., O. BERKU, and T. KALENTSOVA. FORMATION OF THE FOUNDATIONS OF THE CONTEXTUAL APPROACH IN HIGHER DOMESTIC FOREIGN LANGUAGE EDUCATION IN THE 1970-1980S OF THE XX CENTURY. Science and Innovation Center Publishing House, 2021. http://dx.doi.org/10.12731/2658-4034-2021-12-4-2-59-66.
Повний текст джерела